Missing Games Affects WR 2016 Part 1

By John Bush

Missing Games Effects using data from 2016 Wide Receivers Introduction.

The question of the effect on a player’s seasonal value vs the number of missing games they experienced is a hot question! Zeke Elliot is on everyone’s mind and we all await the appeal hearings. I have the answer for the question if he missed 6, 4 , 2, 1 or 0 games what should be his value. That will be shown in the last figure.

WR Performance Raw and Scaled Averages by Team.

I begin this article’s journey by looking at 2016 Team WR data. Teams that use their wide receivers and are passing biased would be more affected by WR’s missing games. I will save that research for later.

The data in Figure 1 lists the 2016 WRs from Team Averages high to low. Note that this figure has been set into “similarity tiers”. (Color Coded Green to Red and Blue to Red – High to Low) .

GB and No stand alone in tier 1 team WR averages, followed by MIA, PIT, NYG, Ind, NE, ATL and SD. These data are clearly on the mind of all 2017 drafters as many of the top WRs are to be found in the top 3 tiers. The bottom tiers include BUF and SF as well as a bottom group of the NYJ, BAL, CLE, HOU, LAR, and PHI. I should not need to emphasize caution while drafting at the bottom.

Note that in my articles I refer to performance scoring. I have invented a metric that can be used across positions on a scale of 0 to 100. My PF is based on this metric. I use to compare players under experimental conditions.

Figure 1. 2016 WR Performance Raw and Scaled Averages by Team.

I try to give visual views of the data landscapes that I am researching. Using colors and labels provides a sobering view of the Team WRs using 2016’s data as seen in Figure 2.

Figure 2. Area Graph WR Data Figure 1

2016 Seasonal Averages of WR’s Performances by Weeks

2017 drafters are not on average thinking about the seasonal landscapes of WRs they are drafting. They not only need Team Biases but they need a landscape view of WRs averages.

The data in Figure 3 when analyzed suggests that you can think of the WR position as gentle stepping stones. The early 5 weeks are above the seasonal average followed by weeks 6 to 9 that are below average, weeks 10 to 15 are the peak times for WRs with a final drop down in weeks 16 and 17.

I suggest our playoffs, if these observations are true should be Weeks 14 and 15 and not include weeks 16 and 17. Also this data predicts that on the average your WRs will struggle in the league finals. Prep now for that. The SOS for week 16 for WR might be a higher priority than suggested by others.

Figure 3 Average of WR PF Raw data and Line Graph.

Effects of Missing Games

In the following Figures 4 to 10, the WRs are grouped by number of games played. Also included are the 2016 weekly PF Score of those WRs leading to their total scores and average scoring by performance. These numbers are color coded by Green/Blue (high) to Red (low). The idea of any player delivering a steady PF score should be removed from your thinking. ADP are an average based on average 2016 PF scoring plus other factors.

As you scan the weekly landscapes of PF scoring, I would “see” what players peaked early vs late. Players that had multiple poor scoring as well. For example, the hot WR, Jamison Crowder did play all 16 games (good) but the last 4 games of his were poor (not good). Does he “tire” out? Watch for signs late in 2017 for a repeat. I suggest using this data to add note to your draft cheat sheets.